This invention relates in general to medical imaging, and more particularly to an improved segmentation method for 3-dimensional ultrasound (3-D US).
The utility of ultrasonography in the diagnosis and assessment of carotid disease is well established. Because of its non-invasive nature and continuing improvements in image quality and Doppler information, ultrasonography is becoming increasingly popular in such applications. In 1991, the NASCET results demonstrated that an angiographic stenosis of xe2x89xa770% selected a group of patients that benefited from carotid endarterectomy, but no equivalent Doppler measurement could satisfactorily select this group (see R. N. Rankin, A. J. Fox, K. Thorpe, and NASCET collaborators, xe2x80x9cCarotid ultrasound: correlation with angiography in a multicenter trial,xe2x80x9d presented to the American Society of Neuroradiology, St. Louis: May 31-Jun. 5 (1992)). Although the role of ultrasound as a screening tool is well established, its role as the only definitive diagnostic test in assessing risk for stroke before surgery is very controversial and subject to heated debate. Nevertheless, there is agreement that factors related to the flexibility of ultrasonography and its high machine and operator dependence have contributed to disappointing results in some tests and trials. Non-standardized techniques and improper choice of equipment yield variable results, particularly in multi-center trials. In addition, measurement choices have in some circumstances been other than optimal. Flow-velocity-based measurements of stenosis severity are subject to a great deal of variability due to measurement location and angle uncertainty, as well as variations in operator skill. These variabilities and inaccuracies are further increased because the measurement of a single velocity vector component at one or two locations in a vessel constitutes only an indirect measure of the risk of stroke.
It has been speculated that 3-D US with real-time visualization of plaque and its surface, as well as 3-D measurements of the stenosis and the actual atheroma volume would reduce the variability of carotid disease assessment and improve diagnosis (see (1) T. S. Hatsukami, B. D. Thackray, and J. F. Primozich, xe2x80x9cEcholucent regions in carotid plaque: Preliminary analysis comparing three-dimensional histologic reconstructions to sonographic findings,xe2x80x9d Ultrasound Med and Biol 20, 743-749 (1994); (2) P H Arbeille, C. Desombre, B. Aesh. M. Philippot, and F. Lapierre, xe2x80x9cQuantification and assessment of carotid artery lesions: degree of stenosis and plaque volume,xe2x80x9d J Clin Ultrasound 123, 113-124 (1995); (3) W. Steinke, and M. Hennerici, xe2x80x9cThree-dimensional ultrasound imaging of carotid artery plaques,xe2x80x9d Journal of Cardiovascular Technology 8, 15-22 (1989); and (4) D. D. McPherson, xe2x80x9cThree-dimensional arterial imaging,xe2x80x9d Scientific American Science and Medicine 22-31, (March/April 1996)).
In addition, 3-D imaging of plaque may also allow quantitative monitoring of plaque development (such as changes in volume and morphology), provide important information about the natural history of atheroma growth, and help in the identification of those plaques which represent a risk of giving rise to stroke.
With a 3-D ultrasound image of the carotid arteries, important information about the vessels, which previously was difficult or impossible to obtain accurately and reliably, can now be ascertained after examination of a patient. In the measurement of the stenosis, the diagnostician can step through the 3-D image, one slice at a time, and outline the edges of the vessel wall. Counting the number of pixels enclosed within the trace and multiplying by the area of a pixel gives the cross-sectional area of the vessel. However, this manual process is highly labour intensive and operator dependent. It has been recognized that a preferable approach is to utilize automatic segmenting of the vessel.
Much work has been reported on the semi-automated segmentation of ultrasound images. This includes work on left ventricle boundary detection in echocardiographic images, ovarian follicle extraction, and intravascular ultrasound segmentation. While many of these studies have produced useful results, nearly all require the user to specify an initial contour, giving rise to inconsistent and mixed results from user to user. Additionally, most are computationally intensive and cannot be practically applied to a large 3-D image.
According to the present invention, an automated segmentation method is provided for three-dimensional vascular ultrasound images. The method includes two steps: an automated initial contour identification, followed by application of a geometrically deformable model (GDM). The formation of the initial contours involves the input of a single seed point by the user, and has been found to be insensitive to the placement of the seed within a structure. The GDM minimizes contour energy, providing a smoothed final result, with only three simple parameters being required as easily selectable input values. The method according to the present invention is fast (capable of performing segmentation on a 336xc3x97352xc3x97200 volume in 25 seconds when running on a 100 MHz 9500 Power Macintosh prototype) and involves minimal user interaction and minimal processing. The inventive method addresses the prior art problem of variability created through user interaction, particularly due to the definition of initial contours and the choice of threshold parameters.